Land Surface Water Index

The Land Surface Water Index (LSWI) was developed by Xiao et al. to monitor vegetation and soil water content. LSWI is sensitive to liquid water in vegetation due to strong SWIR absorption by water. It is widely used for drought monitoring, water stress detection, and integration into vegetation productivity models. Water indices exploit the strong absorption of shortwave infrared and near-infrared radiation by liquid water. They are critical for delineating water bodies, assessing moisture stress in vegetation, and monitoring hydrological changes over time. LSWI is particularly suited for vegetation water content monitoring, drought detection, water stress assessment. The general formula is (NIR - SWIR) / (NIR + SWIR), which requires NIR and SWIR spectral bands.

LSWI is supported by 3 satellite sensors in our database, including Landsat 8/9, SuperView-2, WorldView 3. Each sensor uses different band designations — for example, Landsat 8/9 uses the formula (B5 - B6) / (B5 + B6), while SuperView-2 uses (NIR1 - SWIR) / (NIR1 + SWIR). Select a sensor above to see its specific band mapping.

LSWI requires NIR (841-876), SWIR (1628-1652). Water absorbs strongly in the near-infrared and shortwave infrared portions of the spectrum, creating a measurable contrast with shorter wavelengths that penetrate the water surface.

Both Python and R code samples are provided above. In Python, use rasterio to load individual band GeoTIFF files and numpy for the arithmetic. In R, the terra package handles raster operations efficiently. The key is to load bands as floating-point arrays to avoid integer division, and to handle division-by-zero cases where the denominator equals zero. For production use, consider applying a valid data mask to exclude no-data pixels before calculation.

LSWI is designed to enhance water features, but dark surfaces like shadows, asphalt, and dark soils can produce similar values. For reliable water mapping, consider combining LSWI with a threshold analysis and, where possible, a secondary index to reduce false positives. Time-series analysis can also help distinguish permanent water bodies from temporary dark surfaces.